2024-06-18
Assistant Professor at Vassar College/Yale School of Public Health
Teach statistical modeling and study design
Research focus on infectious disease study design and cluster-randomized trials
Allows use of routinely-collected data
Evaluates interventions in-context
Provides “real world evidence”/population impact
Answers questions randomized trials and observational studies cannot
But … has threats to internal and external validity
8:45–9:15 Introduction to difference-in-differences
9:15–9:40 Analysis 1: DID of zika impacts
9:40–10:05 Advanced DID and staggered adoption
10:05–10:30 Analysis 2: Advanced DID of COVID-19 vaccine mandates
10:35–11:05 Introduction to synthetic control
11:05–11:30 Analysis 3: SC of Ohio’s COVID-19 vaccine lottery
11:30–11:55 Advanced SC methods
11:55–12:20 Analysis 4: Advanced SC of multiple states’ COVID-19 vaccine lotteries
12:20–12:30 Conclusion
Understand, interpret, and critique the use of DID and SC in epidemiology
Contextualize the assumptions needed for causal inference from quasi-experiments
Implement DID and SC analyses and diagnostics/inference in R
Gain familiarity with state-of-the-art methods related to DID and SC and identify resources for further exploration
I will focus here on infectious disease examples from published literature with available data. Some issues are specific to ID, while others are not, but they illustrate the points of how to approach these questions.
All materials: https://github.com/leekshaffer/Epi-QEs/